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Suxin Guo, Researcher, eBay at MLconf SEA - 5/20/16

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Iron: Keyword Grouping Model for Text Ads Bidding in Paid Search: We present a keyword grouping model named Iron that helps determine keyword bidding prices for text ads auctions in Google paid search. In the grouping process, we first train a Gradient Boosting Machine to predict the conversion rate of every keyword based on eBay search log data, paid search performance and Google feedback for the keywords, then calculate the expected value of each keyword based on the predicted conversion rate and the price, and finally group keywords according to the expected value. After that, we set the bids of keywords based on group-level data. We demonstrate that the Iron model improves our incremental revenue greatly compared with the previous keyword grouping model, which groups keywords solely based on keyword category.

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Suxin Guo, Researcher, eBay at MLconf SEA - 5/20/16

  1. 1. Iron: Keyword Grouping Model for Text Ads Bidding in Paid Search May 20, 2016 Suxin Guo
  2. 2. Google Paid Search – Text Ads and Product Listing Ads Traffic Science – Text Ads Bidding 2 Text Ads PLA
  3. 3. Keyword Bidding – Data Scarcity KW portfolio KW with impressions KW with clicks KW with conversions Traffic Science – Text Ads Bidding 3
  4. 4. Text Ads Bidding Process Traffic Science – Text Ads Bidding 4
  5. 5. Algorithm Structure • Conversion rate prediction • Revenue Prediction/Grouping • Market-based feedback loop Traffic Science – Text Ads Bidding 5
  6. 6. Data Pipeline Traffic Science – Text Ads Bidding 6 Keyword Features eBay Search Data Paid Search Performance Google Feedback Gradient Boosting Model Predicted Conversion Rate Keyword Price Parameter Set Φ Estimated Revenue
  7. 7. Overall performance: ROI significantly increases by 27.7% Traffic Science – Text Ads Bidding 7
  8. 8. Thank you! PRESENTATION TITLE GOES HERE 8

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